A Systematic Literature Review of Medical Image Analysis Using Deep Learning

Computer-assisted algorithms for the analysis of medical images require human interactions to achieve satisfying results. Human-based computation and crowdsourcing offer a solution to this problem. We performed a systematic literature review of studies on crowdsourcing human-based computation for medical image analysis based on the guidelines proposed by Kitchenham and Charters. We identified 43 studies relevant to the objective of this research. We determined three primary purposes and problems that crowdsourcing human-based computation systems can solve. We found that the users provided five information types. We compared systems that use pre-, post-evaluation and quality control methods to select and filter the user inputs. We analyzed the metrics used for the evaluation of the crowdsourcing human-based computation system performance. Finally, we identified the most popular crowdsourcing human-based computation platforms with their advantages and disadvantages.Crowdsourcing human-based computation systems can successfully solve medical image analysis problems. However, the application of crowdsourcing human-based computation systems in this research area is still limited and more studies should be conducted to obtain generalizable results. We provided guidelines to practitioners and researchers based on the results obtained in this research.

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pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105796 ◽

2021 ◽

Vol 198 ◽

pp. 105796

Author(s):

Alain Jungo ◽

Olivier Scheidegger ◽

Mauricio Reyes ◽

Fabian Balsiger

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Medical Image ◽

Medical Image Analysis ◽

Data Handling ◽

Python Package

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Collaborative Deep Learning for Medical Image Analysis with Differential Privacy

2019 IEEE Global Communications Conference (GLOBECOM) ◽

10.1109/globecom38437.2019.9014259 ◽

2019 ◽

Author(s):

Danni Yuan ◽

Xiaoyan Zhu ◽

Mingkui Wei ◽

Jianfeng Ma

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Medical Image ◽

Differential Privacy ◽

Medical Image Analysis

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Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

Journal of Imaging ◽

10.3390/jimaging7020019 ◽

2021 ◽

Vol 7 (2) ◽

pp. 19

Author(s):

Tirivangani Magadza ◽

Serestina Viriri

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Brain Tumor ◽

Quantitative Analysis ◽

Medical Image ◽

State Of The Art ◽

Medical Image Analysis ◽

Building Blocks ◽

Tumor Segmentation ◽

Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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